Final Project: Predicting Manhattan Home Sales
Data Cleaning and Wrangling
Data Cleaning
manhattan <- read.csv("https://raw.githubusercontent.com/lcao21/GIS3_Final_Project/master/Data/manhattan.csv", stringsAsFactors=FALSE)
head(manhattan)## BOROUGH NEIGHBORHOOD BUILDING.CLASS.CATEGORY TAX.CLASS.AT.PRESENT
## 1 1 ALPHABET CITY 01 ONE FAMILY DWELLINGS 1
## 2 1 ALPHABET CITY 01 ONE FAMILY DWELLINGS 1
## 3 1 ALPHABET CITY 02 TWO FAMILY DWELLINGS 1
## 4 1 ALPHABET CITY 03 THREE FAMILY DWELLINGS 1
## 5 1 ALPHABET CITY 03 THREE FAMILY DWELLINGS 1
## 6 1 ALPHABET CITY 07 RENTALS - WALKUP APARTMENTS 2
## BLOCK LOT EASE.MENT BUILDING.CLASS.AT.PRESENT ADDRESS
## 1 376 43 NA S1 743 EAST 6TH STREET
## 2 390 61 NA A4 189 EAST 7TH STREET
## 3 404 1 NA B9 166 AVENUE A
## 4 377 56 NA C0 263 EAST 7TH STREET
## 5 393 9 NA C0 604 EAST 11TH STREET
## 6 372 23 NA C1 300 EAST 3RD STREET
## APARTMENT.NUMBER ZIP.CODE RESIDENTIAL.UNITS COMMERCIAL.UNITS TOTAL.UNITS
## 1 10009 1 1 2
## 2 10009 1 0 1
## 3 10009 2 0 2
## 4 10009 3 0 3
## 5 10009 3 0 3
## 6 10009 12 0 12
## LAND.SQUARE.FEET GROSS.SQUARE.FEET YEAR.BUILT TAX.CLASS.AT.TIME.OF.SALE
## 1 2,090 3,680 1940 1
## 2 987 2,183 1860 1
## 3 1,510 4,520 1900 1
## 4 2,430 3,600 1899 1
## 5 2,375 5,110 1939 1
## 6 2,393 7,989 2001 2
## BUILDING.CLASS.AT.TIME.OF.SALE SALE.PRICE SALE.DATE
## 1 S1 3,200,000 7/24/19
## 2 A4 0 9/25/19
## 3 B9 0 7/22/19
## 4 C0 6,300,000 4/30/19
## 5 C0 0 10/24/19
## 6 C1 1,950,000 8/8/19Remove unimportant columns
drops <- c("BOROUGH","EASE.MENT", "TAX.CLASS.AT.PRESENT", "BUILDING.CLASS.AT.PRESENT", "APARTMENT.NUMBER")
manhattan <- manhattan[ , !(names(manhattan) %in% drops)]
head(manhattan)## NEIGHBORHOOD BUILDING.CLASS.CATEGORY BLOCK LOT ADDRESS
## 1 ALPHABET CITY 01 ONE FAMILY DWELLINGS 376 43 743 EAST 6TH STREET
## 2 ALPHABET CITY 01 ONE FAMILY DWELLINGS 390 61 189 EAST 7TH STREET
## 3 ALPHABET CITY 02 TWO FAMILY DWELLINGS 404 1 166 AVENUE A
## 4 ALPHABET CITY 03 THREE FAMILY DWELLINGS 377 56 263 EAST 7TH STREET
## 5 ALPHABET CITY 03 THREE FAMILY DWELLINGS 393 9 604 EAST 11TH STREET
## 6 ALPHABET CITY 07 RENTALS - WALKUP APARTMENTS 372 23 300 EAST 3RD STREET
## ZIP.CODE RESIDENTIAL.UNITS COMMERCIAL.UNITS TOTAL.UNITS LAND.SQUARE.FEET
## 1 10009 1 1 2 2,090
## 2 10009 1 0 1 987
## 3 10009 2 0 2 1,510
## 4 10009 3 0 3 2,430
## 5 10009 3 0 3 2,375
## 6 10009 12 0 12 2,393
## GROSS.SQUARE.FEET YEAR.BUILT TAX.CLASS.AT.TIME.OF.SALE
## 1 3,680 1940 1
## 2 2,183 1860 1
## 3 4,520 1900 1
## 4 3,600 1899 1
## 5 5,110 1939 1
## 6 7,989 2001 2
## BUILDING.CLASS.AT.TIME.OF.SALE SALE.PRICE SALE.DATE
## 1 S1 3,200,000 7/24/19
## 2 A4 0 9/25/19
## 3 B9 0 7/22/19
## 4 C0 6,300,000 4/30/19
## 5 C0 0 10/24/19
## 6 C1 1,950,000 8/8/19Keep only rows for family dwellings
library(data.table)
manhattan <- manhattan[manhattan$BUILDING.CLASS.CATEGORY %like% "FAMILY", ]Keep only rows that have non-zero values for sale price
manhattan <-manhattan[!(manhattan$"SALE.PRICE"== 0),]
replace_commas<-function(x){
x<-as.numeric(gsub("\\,", "", x))
}
manhattan$SALE.PRICE <- replace_commas(manhattan$SALE.PRICE)
manhattan$SALE.PRICE <- as.numeric(manhattan$SALE.PRICE)Geocoding
Clean addresses
library(stringr)
# Convert addresses to type character
manhattan$ADDRESS <- as.character(manhattan$ADDRESS)
# Remove anything before a dash, before a slash, and after a comma
manhattan$ADDRESS <- gsub(".*-","", manhattan$ADDRESS)
manhattan$ADDRESS <- gsub(".*/","", manhattan$ADDRESS)
manhattan$ADDRESS <- gsub("(.*),.*", "\\1", manhattan$ADDRESS)
# Remove rows with address lengths less than 3
manhattan$address_length <- c(str_count(manhattan$ADDRESS))
manhattan <- manhattan[!(manhattan$address_length < 3),]
# Add ", Manhattan" to each address
manhattan$ADDRESS <- paste(manhattan$ADDRESS, ", Manhattan", sep="")library(ggmap)
library(OpenStreetMap)
library(tmaptools)
geocoded_addresses <- geocode_OSM(manhattan$ADDRESS, as.data.frame = TRUE)
geocoded_addresses <- geocoded_addresses[c("query","lat", "lon")]
head(geocoded_addresses)
write.csv(geocoded_addresses,"geocoded_addresses.csv", row.names = FALSE)Since running the above chunk took a very long time, I exported the dataframe output as a csv called “geocoded_addresses.csv”, so that I can use the csv whenever I need to re-run the markdown file without having to wait >= 5 minutes.
geocoded_addresses <- read.csv("https://raw.githubusercontent.com/lcao21/GIS3_Final_Project/master/Data/geocoded_addresses.csv")
head(geocoded_addresses)## query lat lon
## 1 743 EAST 6TH STREET, Manhattan 40.72290 -73.97740
## 2 189 EAST 7TH STREET, Manhattan 40.72493 -73.98051
## 3 166 AVENUE A, Manhattan 40.72811 -73.98174
## 4 263 EAST 7TH STREET, Manhattan 40.72363 -73.97741
## 5 604 EAST 11TH STREET, Manhattan 40.72712 -73.97916
## 6 483 WEST 22ND STREET, Manhattan 40.74700 -74.00411The geocode_OSM() function couldn’t find a few addresses that aren’t huge losses so I’ll ignore those lost rows and merge the geocoded_addresses df with my manhattan df. In the case, since manhattan has more rows than geocoded_addresses, I conduct an “inner join” with merge():
library(dplyr)
# Inner join
manhattan <- merge(manhattan, geocoded_addresses, by.x = "ADDRESS", by.y = "query")# Drop address_length column
manhattan <- manhattan[ , !(names(manhattan) %in% c("address_length"))]
head(manhattan)## ADDRESS NEIGHBORHOOD
## 1 1 SUTTON PLACE, Manhattan MIDTOWN EAST
## 2 106 EAST 78TH STREET, Manhattan UPPER EAST SIDE (59-79)
## 3 109 WEST 11TH STREET, Manhattan GREENWICH VILLAGE-WEST
## 4 11 EAST 129 STREET, Manhattan HARLEM-CENTRAL
## 5 11 WEST 12TH STREET, Manhattan GREENWICH VILLAGE-CENTRAL
## 6 110 WASHINGTON PLACE, Manhattan GREENWICH VILLAGE-WEST
## BUILDING.CLASS.CATEGORY BLOCK LOT ZIP.CODE RESIDENTIAL.UNITS
## 1 01 ONE FAMILY DWELLINGS 1372 26 10022 1
## 2 01 ONE FAMILY DWELLINGS 1412 68 10075 1
## 3 01 ONE FAMILY DWELLINGS 607 50 10011 1
## 4 01 ONE FAMILY DWELLINGS 1754 8 10035 1
## 5 01 ONE FAMILY DWELLINGS 576 48 10011 1
## 6 03 THREE FAMILY DWELLINGS 592 14 10014 3
## COMMERCIAL.UNITS TOTAL.UNITS LAND.SQUARE.FEET GROSS.SQUARE.FEET YEAR.BUILT
## 1 0 1 1,434 5,339 1920
## 2 1 2 1,839 4,344 1899
## 3 0 1 1,665 4,060 1899
## 4 0 1 1,081 2,800 1905
## 5 0 1 2,581 8,125 1910
## 6 0 3 1,575 2,646 1899
## TAX.CLASS.AT.TIME.OF.SALE BUILDING.CLASS.AT.TIME.OF.SALE SALE.PRICE SALE.DATE
## 1 1 A7 13000000 10/8/19
## 2 1 S1 7200000 6/10/19
## 3 1 A4 11200000 5/22/19
## 4 1 A4 1550000 8/22/19
## 5 1 A4 19900000 6/14/19
## 6 1 C0 6800000 4/15/19
## lat lon
## 1 40.75738 -73.96031
## 2 40.77457 -73.96056
## 3 40.73577 -73.99867
## 4 40.75523 -73.96185
## 5 40.73509 -73.99506
## 6 14.27621 120.96607An entry has really suspicious lat and lon columns, as they’re both wildly different than the others:
## manhattan[6, ]$ADDRESS manhattan[6, ]$lat manhattan[6, ]$lon
## 1 110 WASHINGTON PLACE, Manhattan 14.27621 120.9661Using https://www.latlong.net/convert-address-to-lat-long.html, I found the right lat and lon and manually replaced the column values.
manhattan[6,]$lat <- 40.732422
manhattan[6,]$lon <- -74.001152
# Check the min and max of lat and lon that will also be used for mapping
height <- max(manhattan$lat) - min(manhattan$lat)
width <- max(manhattan$lon) - min(manhattan$lon)
borders <- c(bottom = min(manhattan$lat) - 0.2 * height,
top = max(manhattan$lat) + 0.2 * height,
left = min(manhattan$lon) - 0.2 * width,
right = max(manhattan$lon) + 0.2 * width)
borders## bottom top left right
## 40.67401 40.91134 -74.03133 -73.88918Mapping Manhattan homes
library(lubridate)
library(ggplot2)
library(ggrepel)
library(tidyverse)
library(ggmap)
library(osmdata)
map <- get_stamenmap(borders, zoom = 11, maptype = "toner-lite")
ggmap(map) +
geom_point(data = manhattan, mapping = aes(x = lon, y = lat,
col = SALE.PRICE)) +
scale_color_gradient(low = "yellow", high = "red")
I’ll be doing more data exploration once I merge the current manhattan df with the 6 other datasets. I just wanted to output one simple map before doing so. It seems that the most expensive homes are located around Central Park (Upper East Side, Upper West Side), Greenwich Village, and SoHo.
Merging 6 variable datasets
Schools
schools <- read.csv("https://raw.githubusercontent.com/lcao21/GIS3_Final_Project/master/Data/schools.csv", stringsAsFactors = FALSE)
schools$LATITUDE <- as.numeric(schools$LATITUDE)## Warning: NAs introduced by coercionschools$LONGITUDE <- as.numeric(schools$LONGITUDE)## Warning: NAs introduced by coercionRemove rows with zeros for longitude and latitude
schools <-schools[!(schools$"LONGITUDE"== 0),]
schools <- schools[with(schools, order(LONGITUDE)),]Filter for Manhattan schools
m_districts <- c(1,2,3,4,5,6,7,8,9,10)
schools <- schools[schools$Council.district %in% m_districts, ]I found that filtering for council districts was the most accurate way of finding schools located in Manhattan. I initally tried using community district but the community district scale here is much different than the ones I found on the NYC.gov website. According to https://council.nyc.gov/districts/, Manhattan council districts are from 1 to 10 inclusive, though district 8 represents parts of Manhattan and the Bronx.
Next, I want to determine how far each school is from a home property, but for each property, I only care about schools within a certain proximity. Furthermore, since high schools aren’t subject to school zoning laws, distance betwen home and school is weighed more than for middle and elementary schools, which are assigned based on where students’ live. Although school zoning rules probably affect proprty sales (i.e. there’s a premium on property sales near the best public high school in Manhattan), I assume that more weight is put on attending good high schools rather than middle or elementary schools, since the former imapcts college acceptances and thus its quality is more important to parents and students.
Filter out schools that don’t serve grades 9-12
hs_grades <- c("09,10,11,12")
schools <- schools[schools$Grades_text %like% hs_grades, ]Quick map of final schools
ggmap(map) +
geom_point(data = schools, mapping = aes(x = LONGITUDE, y = LATITUDE))
Now that I have the high schools, I want to now know how many of these schools fall within a buffer of a Manhattan home. So, I have to first create buffers around each home, and I’ve decided to create 1609.34 meter (1 mile) buffers. To do so, I had to convert the manhattan df to an sf object with st_as_sf and also transform the original EPSG:4326 CRS to the 2263 projected coordinate system (NAD83/New York Long Island). I also created a column called home_ID that gives each row (home) a unique number. Ultimately, I get an sf object called manhattan_sf_buff that has two fields: home_ID and geometry; geometry is the buffer polygon.
Create 1609.34 meter buffers around homes
library(sf)
library(rgeos)
library(rgdal)
manhattan_copy <- copy(manhattan)
coordinates(manhattan_copy) <- c("lon", "lat")
proj4string(manhattan_copy) <- CRS("+init=epsg:4326")
manhattan_sf <- st_as_sf(manhattan_copy) %>% st_transform(2263) # nad83
manhattan_sf_buff <- st_buffer(manhattan_sf, 1609.34)
manhattan_sf_buff$home_ID <- seq.int(nrow(manhattan_sf_buff))
manhattan_sf_buff <- subset(manhattan_sf_buff, select=c("home_ID", "geometry"))
head(as.data.frame(manhattan_sf_buff))## home_ID geometry
## 1 1 POLYGON ((996855.6 215213.3...
## 2 2 POLYGON ((996782.7 221474.6...
## 3 3 POLYGON ((986228.4 207337.2...
## 4 4 POLYGON ((996430 214430.1, ...
## 5 5 POLYGON ((987229.7 207089.4...
## 6 6 POLYGON ((985540.1 206117.7...I go through a similar process of transforming the schools dataframe of coverting it into an sf object, re-projecting it to the EPSG:2263 projected coordinate system, and creating a column of unique, integer IDs. Ultimately, I get an sf object called schools_sf that has two fields: school_ID and geometry; geometry is of point geometry and represents each school.
schools_copy <- copy(schools)
coordinates(schools_copy) <- c("LONGITUDE", "LATITUDE")
proj4string(schools_copy) <- CRS("+init=epsg:4326")
schools_sf <- st_as_sf(schools_copy) %>% st_transform(2263)
schools_sf$school_ID <- seq.int(nrow(schools_sf))
schools_sf <- subset(schools_sf, select=c("school_ID", "geometry"))
head(as.data.frame(schools_sf))## school_ID geometry
## 1079 1 POINT (978997.2 190477.9)
## 1080 2 POINT (978997.2 190477.9)
## 757 3 POINT (979410.2 196631.7)
## 1113 4 POINT (979859.1 196347.8)
## 1029 5 POINT (980391 200872.7)
## 911 6 POINT (980561.6 196377.9)Plot of all homes and schools
plot(manhattan_sf_buff$geometry, col="Blue")
plot(st_geometry(schools_sf), pch = 3, col = "Red", add= TRUE)
To calculate the intersections between the homes and high schools, I use the st_intersection() function from the sf library.
Calculate intersection
intersection <- st_intersection(x = manhattan_sf_buff, y = schools_sf)## Warning: attribute variables are assumed to be spatially constant throughout all
## geometriesBy plotting the homes and the intersections, we can see a clear decrease in the number of high schools, telling us that only certain schools are within 1609.34 meter buffers of a home.
Plot of all homes and only schools that intersect
plot(manhattan_sf_buff$geometry, col="Blue")
plot(intersection, col = "Red", pch = 3, add = TRUE)## Warning in plot.sf(intersection, col = "Red", pch = 3, add = TRUE): ignoring all
## but the first attribute
Calculate the number of schools within the buffer of each home
int_result <- intersection %>%
group_by(home_ID) %>%
count()
head(int_result)## Simple feature collection with 6 features and 2 fields
## geometry type: GEOMETRY
## dimension: XY
## bbox: xmin: 982544.1 ymin: 203016.9 xmax: 999668.1 ymax: 236294.8
## CRS: EPSG:2263
## # A tibble: 6 x 3
## # Groups: home_ID [6]
## home_ID n geometry
## <int> <int> <GEOMETRY [US_survey_foot]>
## 1 3 2 POINT (985433.1 207572.8)
## 2 5 3 MULTIPOINT ((985433.1 207572.8), (986265.6 207613))
## 3 8 1 POINT (993448.1 229696.9)
## 4 10 5 MULTIPOINT ((982544.1 204889.9), (982848 203278.8), (982949.5 2…
## 5 11 4 MULTIPOINT ((998395 234567.8), (998833.5 235072), (999474.2 236…
## 6 12 4 POINT (991316.2 225687.7)Now we have to left join the manhattan dataframe with int_result. First, I created the same column of unique, integer ID for manhattan so that I can join the two dataframes with the shared home_ID column. After the merge, homes that don’t have schools within its buffer have NA values in the “n” column so I replace those with zeros and rename the column num_schools. I call this final dataframe manhattan_int_schools.
Left join manhattan with int_result
manhattan$home_ID <- seq.int(nrow(manhattan))
manhattan_int_schools <- merge(x = manhattan, y = int_result, by = "home_ID", all.x = TRUE)
manhattan_int_schools$n[is.na(manhattan_int_schools$n)] <- 0
colnames(manhattan_int_schools)[20] <- "num_schools"
head(manhattan_int_schools)## home_ID ADDRESS NEIGHBORHOOD
## 1 1 1 SUTTON PLACE, Manhattan MIDTOWN EAST
## 2 2 106 EAST 78TH STREET, Manhattan UPPER EAST SIDE (59-79)
## 3 3 109 WEST 11TH STREET, Manhattan GREENWICH VILLAGE-WEST
## 4 4 11 EAST 129 STREET, Manhattan HARLEM-CENTRAL
## 5 5 11 WEST 12TH STREET, Manhattan GREENWICH VILLAGE-CENTRAL
## 6 6 110 WASHINGTON PLACE, Manhattan GREENWICH VILLAGE-WEST
## BUILDING.CLASS.CATEGORY BLOCK LOT ZIP.CODE RESIDENTIAL.UNITS
## 1 01 ONE FAMILY DWELLINGS 1372 26 10022 1
## 2 01 ONE FAMILY DWELLINGS 1412 68 10075 1
## 3 01 ONE FAMILY DWELLINGS 607 50 10011 1
## 4 01 ONE FAMILY DWELLINGS 1754 8 10035 1
## 5 01 ONE FAMILY DWELLINGS 576 48 10011 1
## 6 03 THREE FAMILY DWELLINGS 592 14 10014 3
## COMMERCIAL.UNITS TOTAL.UNITS LAND.SQUARE.FEET GROSS.SQUARE.FEET YEAR.BUILT
## 1 0 1 1,434 5,339 1920
## 2 1 2 1,839 4,344 1899
## 3 0 1 1,665 4,060 1899
## 4 0 1 1,081 2,800 1905
## 5 0 1 2,581 8,125 1910
## 6 0 3 1,575 2,646 1899
## TAX.CLASS.AT.TIME.OF.SALE BUILDING.CLASS.AT.TIME.OF.SALE SALE.PRICE SALE.DATE
## 1 1 A7 13000000 10/8/19
## 2 1 S1 7200000 6/10/19
## 3 1 A4 11200000 5/22/19
## 4 1 A4 1550000 8/22/19
## 5 1 A4 19900000 6/14/19
## 6 1 C0 6800000 4/15/19
## lat lon num_schools geometry
## 1 40.75738 -73.96031 0 GEOMETRYCOLLECTION EMPTY
## 2 40.77457 -73.96056 0 GEOMETRYCOLLECTION EMPTY
## 3 40.73577 -73.99867 2 POINT (985433.1 207572.8)
## 4 40.75523 -73.96185 0 GEOMETRYCOLLECTION EMPTY
## 5 40.73509 -73.99506 3 MULTIPOINT ((985433.1 20757...
## 6 40.73242 -74.00115 0 GEOMETRYCOLLECTION EMPTYMap of only homes and schools that intersect
# Schools that intersect with homes
schools_that_int <- as.data.frame(st_transform(intersection, crs = 4326))
schools_that_int <- schools_that_int[!duplicated(schools_that_int[,c("school_ID")]),]
schools$school_ID <- seq.int(nrow(schools))
test2 <- merge(schools, schools_that_int)
head(test2) ## school_ID fiscal_year system_code location_code
## 1 5 2020 02M475 M475
## 2 13 2020 75M721 M721
## 3 14 2020 75M721 M721
## 4 15 2020 02M376 M376
## 5 16 2020 02M615 M615
## 6 17 2020 84M522 M522
## location_name BEDS
## 1 Stuyvesant High School 310200011475
## 2 P.S. M721 - Manhattan Occupational Training Center 307500011721
## 3 P.S. M721 - Manhattan Occupational Training Center 307500011721
## 4 NYC iSchool 310200011376
## 5 Chelsea Career and Technical Education High School 310200011615
## 6 Broome Street Academy Charter School 310200860992
## Managed_by_name location_type_description Location_Category_Description
## 1 DOE General Academic High school
## 2 DOE Special Education Secondary School
## 3 DOE Special Education Secondary School
## 4 DOE General Academic High school
## 5 DOE Career Technical High school
## 6 Charter General Academic High school
## Grades_text Grades_final_text open_date
## 1 09,10,11,12 09,10,11,12 1996-06-05T00:00:00.000
## 2 06,07,08,09,10,11,12,SE 09,10,11,12,SE 1990-12-04T00:00:00.000
## 3 06,07,08,09,10,11,12,SE 09,10,11,12,SE 1990-12-04T00:00:00.000
## 4 09,10,11,12,SE 09,10,11,12 2008-07-01T00:00:00.000
## 5 09,10,11,12,SE 09,10,11,12 1905-07-01T00:00:00.000
## 6 09,10,11,12,SE 09,10,11,12 2011-07-01T00:00:00.000
## Status_descriptions Primary_building_code primary_address_line_1
## 1 Open M477 345 CHAMBERS STREET
## 2 Open M641 250 WEST HOUSTON STREET
## 3 Open M641 250 WEST HOUSTON STREET
## 4 Open M615 131 AVENUE OF THE AMERICAS
## 5 Open M615 131 AVENUE OF THE AMERICAS
## 6 Open MBDF 121 AVENUE OF THE AMERICAS
## State_code X_COORDINATE Y_COORDINATE LONGITUDE LATITUDE Community_district
## 1 NY 980391 200873 -74.01392 40.71802 101
## 2 NY 982544 204890 -74.00616 40.72905 102
## 3 NY 982544 204890 -74.00616 40.72905 102
## 4 NY 982848 203279 -74.00506 40.72463 102
## 5 NY 982848 203279 -74.00506 40.72463 102
## 6 NY 982950 203014 -74.00469 40.72391 102
## Council.district Census_tract Borough_block_lot NTA
## 1 1 31703 1000160215 MN25
## 2 3 6700 1005810054 MN23
## 3 3 6700 1005810054 MN23
## 4 3 3700 1004910016 MN24
## 5 3 3700 1004910016 MN24
## 6 3 3700 0 MN24
## NTA_Name Principal_Name Principal_title
## 1 Battery Park City-Lower Manhattan Eric Contreras Principal
## 2 West Village SHOLOM FRIED Principal
## 3 West Village SHOLOM FRIED Principal
## 4 SoHo-TriBeCa-Civic Center-Little Italy Isora Bailey Principal
## 5 SoHo-TriBeCa-Civic Center-Little Italy Jaivelle Reed Principal
## 6 SoHo-TriBeCa-Civic Center-Little Italy Melissa Silberman Principal
## Principal_phone_number fax_number Geographical_District_code
## 1 212-312-4800 212-587-3874 2
## 2 212-675-7926 212-255-3227 2
## 3 212-675-7926 212-255-3227 2
## 4 917-237-7300 212-219-0743 2
## 5 212-925-1080 212-941-7934 2
## 6 212-453-0295 212-966-7253 2
## Administrative_District_Code Administrative_District_Name
## 1 2 NULL
## 2 75 CITYWIDE SPECIAL EDUCATION
## 3 75 CITYWIDE SPECIAL EDUCATION
## 4 2 NULL
## 5 2 NULL
## 6 84 OFFICE OF CHARTER SCHOOLS
## community_school_sup_name Tier_3_Support_Location_Name
## 1 NULL NYCDOE Borough Office - Manhattan
## 2 LOUISSAINT, KETLER D75 CITYWIDE BCO
## 3 LOUISSAINT, KETLER D75 CITYWIDE BCO
## 4 NULL NYCDOE Borough Office - Manhattan
## 5 NULL NYCDOE Borough Office - Manhattan
## 6 NULL NULL
## Tier_3_Support_Leader_Name Tier_2_Support_Location_Name
## 1 CHU, YUET School Support Team 1- Manhattan
## 2 Tillman Roberto Children First Network 755
## 3 Tillman Roberto Children First Network 755
## 4 CHU, YUET School Support Team 1- Manhattan
## 5 CHU, YUET School Support Team 1- Manhattan
## 6 NULL NULL
## HighSchool_Network_Location_Code HighSchool_Network_Name
## 1 HS04 HS Network 04 Superintendent Office
## 2 NULL NULL
## 3 NULL NULL
## 4 HS04 HS Network 04 Superintendent Office
## 5 HS04 HS Network 04 Superintendent Office
## 6 NULL NULL
## HighSchool_Network_Superintendent Community_district.1 Police_precinct
## 1 ORLEN, VIVIAN 101 1
## 2 NULL 102 6
## 3 NULL 102 6
## 4 ORLEN, VIVIAN 102 1
## 5 ORLEN, VIVIAN 102 1
## 6 NULL 102 1
## home_ID geometry
## 1 130 POINT (-74.01392 40.71802)
## 2 10 POINT (-74.00616 40.72905)
## 3 10 POINT (-74.00616 40.72905)
## 4 10 POINT (-74.00506 40.72463)
## 5 10 POINT (-74.00506 40.72463)
## 6 10 POINT (-74.00469 40.72391)# Homes with at least 1 school in its buffer
test3 <- manhattan_int_schools[manhattan_int_schools$num_schools > 0,]
head(test3)## home_ID ADDRESS NEIGHBORHOOD
## 3 3 109 WEST 11TH STREET, Manhattan GREENWICH VILLAGE-WEST
## 5 5 11 WEST 12TH STREET, Manhattan GREENWICH VILLAGE-CENTRAL
## 8 8 115 MANHATTAN AVENUE, Manhattan MANHATTAN VALLEY
## 10 10 116 SULLIVAN STREET, Manhattan SOHO
## 11 11 116 WEST 130TH STREET, Manhattan HARLEM-CENTRAL
## 12 12 116 WEST 80TH STREET, Manhattan UPPER WEST SIDE (79-96)
## BUILDING.CLASS.CATEGORY BLOCK LOT ZIP.CODE RESIDENTIAL.UNITS
## 3 01 ONE FAMILY DWELLINGS 607 50 10011 1
## 5 01 ONE FAMILY DWELLINGS 576 48 10011 1
## 8 02 TWO FAMILY DWELLINGS 1840 152 10025 2
## 10 02 TWO FAMILY DWELLINGS 504 29 10012 2
## 11 03 THREE FAMILY DWELLINGS 1914 41 10027 3
## 12 01 ONE FAMILY DWELLINGS 1210 142 10024 1
## COMMERCIAL.UNITS TOTAL.UNITS LAND.SQUARE.FEET GROSS.SQUARE.FEET YEAR.BUILT
## 3 0 1 1,665 4,060 1899
## 5 0 1 2,581 8,125 1910
## 8 0 2 900 3,600 1899
## 10 0 2 2,350 5,446 1900
## 11 0 3 1,665 3,476 1910
## 12 0 1 1,839 5,520 1910
## TAX.CLASS.AT.TIME.OF.SALE BUILDING.CLASS.AT.TIME.OF.SALE SALE.PRICE
## 3 1 A4 11200000
## 5 1 A4 19900000
## 8 1 B1 10
## 10 1 B1 13450000
## 11 1 C0 2300000
## 12 1 A4 4675000
## SALE.DATE lat lon num_schools geometry
## 3 5/22/19 40.73577 -73.99867 2 POINT (985433.1 207572.8)
## 5 6/14/19 40.73509 -73.99506 3 MULTIPOINT ((985433.1 20757...
## 8 2/11/20 40.79783 -73.96233 1 POINT (993448.1 229696.9)
## 10 3/2/20 40.72613 -74.00286 5 MULTIPOINT ((982544.1 20488...
## 11 4/19/19 40.81115 -73.94412 4 MULTIPOINT ((998395 234567....
## 12 8/2/19 40.78288 -73.97604 4 POINT (991316.2 225687.7)map2 <- get_stamenmap(borders, zoom = 11, maptype = "terrain")
ggmap(map2) +
geom_point(data = test2, mapping = aes(x = LONGITUDE, y = LATITUDE), col="Red", shape=3) + # Schools
geom_point(data = test3, mapping = aes(x = lon, y = lat), col="Blue") # Homes
Healthy Stores
stores <- read.csv("https://raw.githubusercontent.com/lcao21/GIS3_Final_Project/master/Data/healthystores.csv", stringsAsFactors = FALSE)Remove rows with zeros for longitude and latitude
stores <-stores[!is.na(stores$Longitude), ]Filter for New York stores
stores <- stores[stores$Borough %like% "New York", ]After removing stores without latitude and longitude values and aren’t located in the New York borough, I’m left with 68 stores.
Quick map of final stores
ggmap(map) +
geom_point(data = stores, mapping = aes(x = Longitude, y = Latitude))
It seems that the dataset for grocery stores in New York are all clustered in the Upper East Side. My dataset for Manhattan homes, however, is spread throughout Manhattan so I expect that a lot of homes will have zero stores in their buffers. So, I decided to combine stores with data on farmers markets.
The farmers market data, as shown below, is spread out more evently than stores.
Clean farmers markets data and map it
farmers <- read.csv("https://raw.githubusercontent.com/lcao21/GIS3_Final_Project/master/Data/farmersmarkets.csv", stringsAsFactors = FALSE)
farmers <-farmers[!is.na(farmers$Longitude), ]
farmers <- farmers[farmers$Borough %like% "Manhattan", ]
ggmap(map) +
geom_point(data = farmers, mapping = aes(x = Longitude, y = Latitude))
The final merged dataframe is called food and has four columns: name, street_address, latitude, and longitude.
Combine stores and farmers dataframes
food_names <- c(stores$Store.Name, farmers$Market.Name)
stores$street_address <- paste(stores$Street.Address, stores$Address, sep=" ")
food_addresses <- c(stores$street_address, farmers$Street.Address)
food_lat <- c(stores$Latitude, farmers$Latitude)
food_lon <- c(stores$Longitude, farmers$Longitude)
food <- data.frame("name" = food_names, "street_address" = food_addresses, "latitude" = food_lat, "longitude" = food_lon) Quick map of healthy grocery vendors
ggmap(map) +
geom_point(data = food, mapping = aes(x = longitude, y = latitude))
Since I already created an sf object with 1609.34 meter buffers for each home–manhattan_sf_buff, I can use it again here. Now, I have to transform the food dataframe into an sf object so that I can use st_intersection() between the two.
foods_copy <- copy(food)
coordinates(foods_copy) <- c("longitude", "latitude")
proj4string(foods_copy) <- CRS("+init=epsg:4326")
food_sf <- st_as_sf(foods_copy) %>% st_transform(2263)
food_sf$food_ID <- seq.int(nrow(food_sf))
food_sf <- subset(food_sf, select=c("food_ID", "geometry"))
head(as.data.frame(food_sf))## food_ID geometry
## 1 1 POINT (999869.1 230322.1)
## 2 2 POINT (1000307 230058.2)
## 3 3 POINT (1000599 228063.3)
## 4 4 POINT (1000611 228095)
## 5 5 POINT (1001628 229567.3)
## 6 6 POINT (1000484 227856.3)Plot of all homes and food vendors
plot(manhattan_sf_buff$geometry, col="Blue")
plot(st_geometry(food_sf), pch = 3, col = "Green", add= TRUE)
Calculate intersection
food_intersection <- st_intersection(x = manhattan_sf_buff, y = food_sf)## Warning: attribute variables are assumed to be spatially constant throughout all
## geometriesPlot of all homes and only food vendors that intersect
plot(manhattan_sf_buff$geometry, col="Blue")
plot(food_intersection, col = "Green", pch = 3, add = TRUE)## Warning in plot.sf(food_intersection, col = "Green", pch = 3, add = TRUE):
## ignoring all but the first attribute
Calculate the number of food vendors within the buffer of each home
food_int_result <- food_intersection %>%
group_by(home_ID) %>%
count()
head(food_int_result)## Simple feature collection with 6 features and 2 fields
## geometry type: GEOMETRY
## dimension: XY
## bbox: xmin: 986930.1 ymin: 207834.2 xmax: 1000676 ymax: 235975.3
## CRS: EPSG:2263
## # A tibble: 6 x 3
## # Groups: home_ID [6]
## home_ID n geometry
## <int> <int> <GEOMETRY [US_survey_foot]>
## 1 5 1 POINT (986930.1 207834.2)
## 2 8 1 POINT (995422.1 231161.9)
## 3 9 6 MULTIPOINT ((997335.9 226796), (998309 226256.2), (998421 22664…
## 4 11 2 MULTIPOINT ((998555.1 234012), (1000676 235975.3))
## 5 12 1 POINT (990898 223916.9)
## 6 19 10 MULTIPOINT ((997335.9 226796), (998309 226256.2), (998421 22664…Now I left joined manhattan_int_schools with food_int_result, using home_ID as the mutual column, and renamed the dataframe manhattan_ints because all the dependent variable columns will be in here or eventually merged into it. For rows that have NA values for the column of food vendor counts, I replaced them with zeros.
manhattan_ints <- merge(x = manhattan_int_schools, y = food_int_result, by = "home_ID", all.x = TRUE)
manhattan_ints$n[is.na(manhattan_ints$n)] <- 0
colnames(manhattan_ints)[22] <- "num_food"
head(manhattan_ints)## home_ID ADDRESS NEIGHBORHOOD
## 1 1 1 SUTTON PLACE, Manhattan MIDTOWN EAST
## 2 2 106 EAST 78TH STREET, Manhattan UPPER EAST SIDE (59-79)
## 3 3 109 WEST 11TH STREET, Manhattan GREENWICH VILLAGE-WEST
## 4 4 11 EAST 129 STREET, Manhattan HARLEM-CENTRAL
## 5 5 11 WEST 12TH STREET, Manhattan GREENWICH VILLAGE-CENTRAL
## 6 6 110 WASHINGTON PLACE, Manhattan GREENWICH VILLAGE-WEST
## BUILDING.CLASS.CATEGORY BLOCK LOT ZIP.CODE RESIDENTIAL.UNITS
## 1 01 ONE FAMILY DWELLINGS 1372 26 10022 1
## 2 01 ONE FAMILY DWELLINGS 1412 68 10075 1
## 3 01 ONE FAMILY DWELLINGS 607 50 10011 1
## 4 01 ONE FAMILY DWELLINGS 1754 8 10035 1
## 5 01 ONE FAMILY DWELLINGS 576 48 10011 1
## 6 03 THREE FAMILY DWELLINGS 592 14 10014 3
## COMMERCIAL.UNITS TOTAL.UNITS LAND.SQUARE.FEET GROSS.SQUARE.FEET YEAR.BUILT
## 1 0 1 1,434 5,339 1920
## 2 1 2 1,839 4,344 1899
## 3 0 1 1,665 4,060 1899
## 4 0 1 1,081 2,800 1905
## 5 0 1 2,581 8,125 1910
## 6 0 3 1,575 2,646 1899
## TAX.CLASS.AT.TIME.OF.SALE BUILDING.CLASS.AT.TIME.OF.SALE SALE.PRICE SALE.DATE
## 1 1 A7 13000000 10/8/19
## 2 1 S1 7200000 6/10/19
## 3 1 A4 11200000 5/22/19
## 4 1 A4 1550000 8/22/19
## 5 1 A4 19900000 6/14/19
## 6 1 C0 6800000 4/15/19
## lat lon num_schools geometry.x num_food
## 1 40.75738 -73.96031 0 GEOMETRYCOLLECTION EMPTY 0
## 2 40.77457 -73.96056 0 GEOMETRYCOLLECTION EMPTY 0
## 3 40.73577 -73.99867 2 POINT (985433.1 207572.8) 0
## 4 40.75523 -73.96185 0 GEOMETRYCOLLECTION EMPTY 0
## 5 40.73509 -73.99506 3 MULTIPOINT ((985433.1 20757... 1
## 6 40.73242 -74.00115 0 GEOMETRYCOLLECTION EMPTY 0
## geometry.y
## 1 GEOMETRYCOLLECTION EMPTY
## 2 GEOMETRYCOLLECTION EMPTY
## 3 GEOMETRYCOLLECTION EMPTY
## 4 GEOMETRYCOLLECTION EMPTY
## 5 POINT (986930.1 207834.2)
## 6 GEOMETRYCOLLECTION EMPTYMap of only homes and food vendors that intersect
# Food vendors that intersect with homes
food_that_int <- as.data.frame(st_transform(food_intersection, crs = 4326))
food_that_int <- food_that_int[!duplicated(food_that_int[,c("food_ID")]),]
food$food_ID <- seq.int(nrow(food))
food_merged <- merge(food, food_that_int)
head(food_merged) ## food_ID name street_address latitude
## 1 1 Comidas Dominicanas Capellan Deli 1626 Park Avenue 40.79884
## 2 5 Juice Bar Deli 308 East 116th Street 40.79677
## 3 9 El Tepeyac Grocery 1621 Lexington Avenue 40.78992
## 4 13 JJ Food Market 153 East 99th Street 40.78769
## 5 14 Deli Mini Mart 339 East 115th Street 40.79565
## 6 15 Cherry Deli Gourmet 1575 Lexington Avenue 40.78874
## longitude home_ID geometry
## 1 -73.94359 156 POINT (-73.94359 40.79884)
## 2 -73.93724 93 POINT (-73.93724 40.79677)
## 3 -73.94796 19 POINT (-73.94796 40.78992)
## 4 -73.94923 9 POINT (-73.94923 40.78769)
## 5 -73.93670 93 POINT (-73.9367 40.79565)
## 6 -73.94882 9 POINT (-73.94882 40.78874)# Homes with at least 1 food vendor in its buffer
homes_with_food <- manhattan_ints[manhattan_ints$num_food > 0,]
head(homes_with_food)## home_ID ADDRESS NEIGHBORHOOD
## 5 5 11 WEST 12TH STREET, Manhattan GREENWICH VILLAGE-CENTRAL
## 8 8 115 MANHATTAN AVENUE, Manhattan MANHATTAN VALLEY
## 9 9 116 EAST 95TH STREET, Manhattan UPPER EAST SIDE (79-96)
## 11 11 116 WEST 130TH STREET, Manhattan HARLEM-CENTRAL
## 12 12 116 WEST 80TH STREET, Manhattan UPPER WEST SIDE (79-96)
## 19 19 1255 PARK AVENUE, Manhattan HARLEM-EAST
## BUILDING.CLASS.CATEGORY BLOCK LOT ZIP.CODE RESIDENTIAL.UNITS
## 5 01 ONE FAMILY DWELLINGS 576 48 10011 1
## 8 02 TWO FAMILY DWELLINGS 1840 152 10025 2
## 9 01 ONE FAMILY DWELLINGS 1523 68 10128 1
## 11 03 THREE FAMILY DWELLINGS 1914 41 10027 3
## 12 01 ONE FAMILY DWELLINGS 1210 142 10024 1
## 19 02 TWO FAMILY DWELLINGS 1625 1 10029 2
## COMMERCIAL.UNITS TOTAL.UNITS LAND.SQUARE.FEET GROSS.SQUARE.FEET YEAR.BUILT
## 5 0 1 2,581 8,125 1910
## 8 0 2 900 3,600 1899
## 9 0 1 2,013 4,160 1899
## 11 0 3 1,665 3,476 1910
## 12 0 1 1,839 5,520 1910
## 19 0 2 847 1,650 1920
## TAX.CLASS.AT.TIME.OF.SALE BUILDING.CLASS.AT.TIME.OF.SALE SALE.PRICE
## 5 1 A4 19900000
## 8 1 B1 10
## 9 1 A9 8000000
## 11 1 C0 2300000
## 12 1 A4 4675000
## 19 1 B1 906500
## SALE.DATE lat lon num_schools geometry.x
## 5 6/14/19 40.73509 -73.99506 3 MULTIPOINT ((985433.1 20757...
## 8 2/11/20 40.79783 -73.96233 1 POINT (993448.1 229696.9)
## 9 3/20/20 40.78548 -73.95259 0 GEOMETRYCOLLECTION EMPTY
## 11 4/19/19 40.81115 -73.94412 4 MULTIPOINT ((998395 234567....
## 12 8/2/19 40.78288 -73.97604 4 POINT (991316.2 225687.7)
## 19 2/7/20 40.78720 -73.95172 0 GEOMETRYCOLLECTION EMPTY
## num_food geometry.y
## 5 1 POINT (986930.1 207834.2)
## 8 1 POINT (995422.1 231161.9)
## 9 6 MULTIPOINT ((997335.9 22679...
## 11 2 MULTIPOINT ((998555.1 23401...
## 12 1 POINT (990898 223916.9)
## 19 10 MULTIPOINT ((997335.9 22679...ggmap(map2) +
geom_point(data = food_merged, mapping = aes(x = longitude, y = latitude), col="Green", shape=3) + # Food
geom_point(data = homes_with_food, mapping = aes(x = lon, y = lat), col="Blue") # Homes
I repeat the whole process again but for the parks dataset this time. Unlike schools, stores, and farmers’ markets, however, parks aren’t represent as points but rather as polygons, and I loaded them in as geojson file.
Parks
library(geojsonsf)
parks <-geojson_sf("parks.geojson")
parks$park_ID <- seq.int(nrow(parks))
head(as.data.frame(parks))## system shape_leng shape_area parknum landuse
## 1 <NA> 829.420106841 41539.8013629 B021 Community Park
## 2 <NA> 607.555193338 21960.4949197 B222 Neighborhood Park
## 3 <NA> 784.42199003 28079.0762647 B326 Neighborhood Park
## 4 <NA> 2672.71014961 438416.114511 B021 Community Park
## 5 <NA> 1376.10639843 14465.699799 B223DG Triangle/Plaza
## 6 <NA> 1218.55982108 54510.4377633 B223K Parkway
## park_name sub_code feat_code status source_id
## 1 Commodore Barry Park 491050 4910 Updated 21491000001
## 2 Pierrepont Playground 498000 4980 Unchanged 21498000002
## 3 Cobble Hill Park 498000 4980 Unchanged 21498000003
## 4 Commodore Barry Park 498000 4980 Unchanged 21498000004
## 5 Brooklyn Heights Promenade 498000 4980 Unchanged 21498000005
## 6 Trinity Park 498000 4980 Unchanged 21498000015
## geometry park_ID
## 1 MULTIPOLYGON (((-73.97922 4... 1
## 2 MULTIPOLYGON (((-73.99732 4... 2
## 3 MULTIPOLYGON (((-73.99557 4... 3
## 4 MULTIPOLYGON (((-73.97747 4... 4
## 5 MULTIPOLYGON (((-73.99747 4... 5
## 6 MULTIPOLYGON (((-73.98416 4... 6There isn’t a column for borough, so I can’t filter for only parks in Manhattan. Nevertheless, this shouldn’t be a problem once I calculate the intersections. First, I have to re-project the parks data.
Re-project parks so that it has the same crs (2263) as the homes
parks_copy <- copy(parks)
parks_sf <- st_transform(parks_copy, crs = 2263)Plot all homes and parks
plot(manhattan_sf_buff$geometry, col="Blue")
plot(st_geometry(parks_sf), col = "darkseagreen1", add= TRUE)
Calculate intersection
parks_intersection <- st_intersection(x = manhattan_sf_buff, y = parks_sf)## Warning: attribute variables are assumed to be spatially constant throughout all
## geometriesPlot of all homes and only parks that intersect
plot(manhattan_sf_buff$geometry, col="Blue")
plot(parks_intersection, col = "darkseagreen1", add = TRUE)## Warning in plot.sf(parks_intersection, col = "darkseagreen1", add = TRUE):
## ignoring all but the first attribute
Calculate the number of parks within the buffer of each home
parks_int_result <- parks_intersection %>%
group_by(home_ID) %>%
count()
head(parks_int_result)## Simple feature collection with 6 features and 2 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: 982567.6 ymin: 204537.8 xmax: 996362.5 ymax: 222983.7
## CRS: EPSG:2263
## # A tibble: 6 x 3
## # Groups: home_ID [6]
## home_ID n geometry
## <int> <int> <MULTIPOLYGON [US_survey_foot]>
## 1 1 18 (((994393.8 213848.9, 994369.7 213863.6, 994359 213870.9, 99440…
## 2 2 14 (((994251.7 220156, 994232.2 220169.3, 994234.2 220172.9, 99423…
## 3 3 20 (((985002 205774.6, 984953.7 205763, 984870.8 205747.7, 984787.…
## 4 4 13 (((993976 213408.1, 993977.7 213405.8, 993979.3 213403.4, 99398…
## 5 5 5 (((985441.6 205490.2, 985368.6 205499.8, 985285.8 205515.2, 985…
## 6 6 41 (((983626.9 204537.8, 983605.5 204541.8, 983576.8 204559.4, 983…The counts seem really high but if we look at parks_intersection, we see that some of the parks are being counted multiple times for the same parks. This is because the original dataset assigns different shape_area, system, source_id, parknum, and shape_leng columns to some of the same parks. So even after applying the unique() function, I get the same rows.
nrow(as.data.frame(unique(parks_intersection))) == nrow(as.data.frame(parks_intersection))## [1] TRUEI add the counts of parks that intersect with home buffers to the main dataframe: manhattan_ints.
Left join manhattan_ints with parks_int_result
manhattan_ints <- merge(x = manhattan_ints, y = parks_int_result, by = "home_ID", all.x = TRUE)
manhattan_ints$n[is.na(manhattan_ints$n)] <- 0
colnames(manhattan_ints)[24] <- "num_parks"
head(manhattan_ints)## home_ID ADDRESS NEIGHBORHOOD
## 1 1 1 SUTTON PLACE, Manhattan MIDTOWN EAST
## 2 2 106 EAST 78TH STREET, Manhattan UPPER EAST SIDE (59-79)
## 3 3 109 WEST 11TH STREET, Manhattan GREENWICH VILLAGE-WEST
## 4 4 11 EAST 129 STREET, Manhattan HARLEM-CENTRAL
## 5 5 11 WEST 12TH STREET, Manhattan GREENWICH VILLAGE-CENTRAL
## 6 6 110 WASHINGTON PLACE, Manhattan GREENWICH VILLAGE-WEST
## BUILDING.CLASS.CATEGORY BLOCK LOT ZIP.CODE RESIDENTIAL.UNITS
## 1 01 ONE FAMILY DWELLINGS 1372 26 10022 1
## 2 01 ONE FAMILY DWELLINGS 1412 68 10075 1
## 3 01 ONE FAMILY DWELLINGS 607 50 10011 1
## 4 01 ONE FAMILY DWELLINGS 1754 8 10035 1
## 5 01 ONE FAMILY DWELLINGS 576 48 10011 1
## 6 03 THREE FAMILY DWELLINGS 592 14 10014 3
## COMMERCIAL.UNITS TOTAL.UNITS LAND.SQUARE.FEET GROSS.SQUARE.FEET YEAR.BUILT
## 1 0 1 1,434 5,339 1920
## 2 1 2 1,839 4,344 1899
## 3 0 1 1,665 4,060 1899
## 4 0 1 1,081 2,800 1905
## 5 0 1 2,581 8,125 1910
## 6 0 3 1,575 2,646 1899
## TAX.CLASS.AT.TIME.OF.SALE BUILDING.CLASS.AT.TIME.OF.SALE SALE.PRICE SALE.DATE
## 1 1 A7 13000000 10/8/19
## 2 1 S1 7200000 6/10/19
## 3 1 A4 11200000 5/22/19
## 4 1 A4 1550000 8/22/19
## 5 1 A4 19900000 6/14/19
## 6 1 C0 6800000 4/15/19
## lat lon num_schools geometry.x num_food
## 1 40.75738 -73.96031 0 GEOMETRYCOLLECTION EMPTY 0
## 2 40.77457 -73.96056 0 GEOMETRYCOLLECTION EMPTY 0
## 3 40.73577 -73.99867 2 POINT (985433.1 207572.8) 0
## 4 40.75523 -73.96185 0 GEOMETRYCOLLECTION EMPTY 0
## 5 40.73509 -73.99506 3 MULTIPOINT ((985433.1 20757... 1
## 6 40.73242 -74.00115 0 GEOMETRYCOLLECTION EMPTY 0
## geometry.y num_parks geometry
## 1 GEOMETRYCOLLECTION EMPTY 18 MULTIPOLYGON (((994393.8 21...
## 2 GEOMETRYCOLLECTION EMPTY 14 MULTIPOLYGON (((994251.7 22...
## 3 GEOMETRYCOLLECTION EMPTY 20 MULTIPOLYGON (((985002 2057...
## 4 GEOMETRYCOLLECTION EMPTY 13 MULTIPOLYGON (((993976 2134...
## 5 POINT (986930.1 207834.2) 5 MULTIPOLYGON (((985441.6 20...
## 6 GEOMETRYCOLLECTION EMPTY 41 MULTIPOLYGON (((983626.9 20...